Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation

a1429b47-8529-41e5-9982-cc20d6c5ac68

Active

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£8,128,325

Jan. 1, 2021

Dec. 31, 2024

The UK, Ireland, Canada and France have all declared climate emergencies. Climate change has never had a more prominent in the public eye. With legal commitments to reduce greenhouse gas emissions by at least 80% by 2050 relative to 1990 levels, it has never been more important to do everything we can to reduce energy demand. The promise in this project is to help provide new methods for analysing the 'data deluge' of energy and building system data (from IoT devices) that can help unlock energy efficiencies and identify the benefits of energy efficiency measures despite noisy and heterogeneous data; and make it cheap, repeatable and routine to do this on an ongoing basis. Key to our approach are novel statistical and mixed-method techniques working closely with our project partners and their data to demonstrate the feasibility of these benefits. Our ultimate goal is to make it possible to translate the savings found in one context to another (e.g. another similar building, or even similar business). This would enable the 'digital replication' of energy efficiency savings, and even an almost viral spread of the knowledge and technique across sectors---with massive potential.

Currently for many organisations, making sense of this rich source of information defies the human resource available to analyse and profit from the potential insights available. Such analysis is currently the domain of specialist consultancy providers due to the significant cost, time and know-how required to identify opportunities in the data. This restricts the penetration of data-driven monitoring and energy reduction strategies, and the opportunities for knowledge transfer across different locations and businesses. This project will clear this analysis bottleneck.

The approach builds on foundations in modern data science, applying cutting edge techniques to automatically identify problems at particular sites and recommend interventions based on cross-site comparisons. The principle objective is to enable commercial sites to reduce their energy demand and keep it low without requiring energy analysts to manually investigate each site individually, at further expense.

Core to our approach are next-generation statistics and machine learning methods applied to a unique corpus of fine-grained energy and process data sourced from our partners (BT, Tesco, Lancaster University Facilities (a town sized campus), and energy management consultancy and cloud energy analytics provider, BEST). This will enable us to apply cutting edge statistical techniques to a very significant data set in this domain for the first time.

More specifically, our main aims are to:

1. develop automated techniques for supporting analysis, identifying and recommending energy savings strategies, based on the application of statistical and machine learning techniques to fine-grained energy data;
2. derive knowledge of how, where and when energy is used, to identify opportunities to reduce and shift demand by comparing differences in energy use over time within and between premises;
3. support regular and repeated analysis, towards a continual improvement in energy reduction over time.
4. provide open source, permissively licensed implementations for enabling uptake, even beyound our project partners and their partner networks. Our publication and publicity strategies will maximise exposure of our project results to various stakeholder groups including academia, practitioners, and key industry stakeholders.

Potential Impact:
The impacts of our project could be substantial. Non-domestic buildings in the UK contribute 18% of UK's greenhouse gas emissions. The commercial sector has risen from 13.3\% to 15.3\% in the last decade, and now represent the most energy intensive portion of the UK's service industry. Given very substantial and growing energy costs, and a more variable and changing energy environment, a digitally replicable energy savings technique has the potential to save millions.

This proposal will benefit a variety of different stakeholders:
(a) Human-kind, through the reduction of carbon emissions as a result of reduced industrial energy demand;
(b) UK society, via lowering commercial operating costs, lowering the demand on the national grid, and contributing toward lower UK emissions and associated targets;
(c) A wide range of business and building types, who will gain tools to reduce their end use energy demand; and the energy analysis consultancies who are in urgent need of tools for reducing the cost of analysing increasingly large and complex volumes of energy and IoT data to avoid energy waste;
(d) Our partners, who represent the sectors described in (c);
(e) The academic research community, particularly in disciplines that underpin and relate to the data sciences;
(f) Project personnel: PDRAs and PhD students, who gain valuable experience from an industry facing multidisciplinary environment.

How will they benefit?
New applied methods: (a-e)
The research will develop a number of state-of-the-art methods to enable a reduction in commercial energy demand, that will be shared with our partners and released to the public domain for commercial and non-commercial expoitation. Our methods will result in efficient and cost-effective ways of processing energy and linked IoT data, and make recommending energy saving strategies automatic and therefore scalable to achieve on a regular basis. These benefits will flow through the economy and society via a number of different mechanisms, including: more efficient use of energy (e.g. better management of energy in a range of commercial business types); improved optimisation and response to anomalous and abnormal energy loads (e.g. via self-comparisons and across similar sites, and more timely intervention in the result of unexpected energy use). Companies will be keen to adopt methodologies with multi-million pound savings potential. We plan to make available documented open source code for others to use commercially or in open-source platforms.

Targeted Knowledge Exchange: (d)
Through partnership on this project: several leading organisations have expressed enthusiastic support for our vision, and provided valuable insight and advice in developing this proposal (e.g. arriving at the idea of `knowledge kitchens', focused knowledge exchange workshops) PDRAs will spend periods of time at partner locations and partner staff will be invited to spend time with the team. The Advisory Board provides a mechanism to help partners work with us to develop successful knowledge exchange mechanisms, and ensure this reaches a considerably wider set of partners (e.g. via links to CREDS, UK ERC, CESI, and the UK Collabatorium for Research in Infrastructures & Cities.

Generic Knowledge Exchange: (e)
We will develop methods that are of considerable interest to academic communities in Data Science, Statistics, Sustainability, Sustainable HCI, and other fields. As well as the traditional routes of journal publication, workshops and conferences the project will develop open source R/ platform software that embodies our methods: these will benefit the academic community and beyond.

Developing good people: (all)
This proposal will secure an increase in the number and quality of researchers focusing on the multi-disciplinary aspects of sustainability and climate change, including in data science, statistics and sustainability, areas of historic shortage and increasing world importance.